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      Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A Comparison of Time Series Forecasting Methods

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          Abstract

          Background

          The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence.

          Methods

          In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020.

          Results

          Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2,484 new confirmed and 114 new death cases of COVID-19.

          Conclusion

          According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

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          Most cited references23

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          Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China

          Abstract To help health workers and the public recognize and deal with the 2019 novel coronavirus (2019‐nCoV) quickly, effectively, and calmly with an updated understanding. A comprehensive search from Chinese and worldwide official websites and announcements was performed between 1 December 2019 and 9:30 am 26 January 2020 (Beijing time). A latest summary of 2019‐nCoV and the current outbreak was drawn. Up to 24 pm, 25 January 2020, a total of 1975 cases of 2019‐nCoV infection were confirmed in mainland China with a total of 56 deaths having occurred. The latest mortality was approximately 2.84% with a total of 2684 cases still suspected. The China National Health Commission reported the details of the first 17 deaths up to 24 pm, 22 January 2020. The deaths included 13 males and 4 females. The median age of the people who died was 75 (range 48‐89) years. Fever (64.7%) and cough (52.9%) were the most common first symptoms among those who died. The median number of days from the occurence of the first symptom to death was 14.0 (range 6‐41) days, and it tended to be shorter among people aged 70 years or more (11.5 [range 6‐19] days) than those aged less than 70 years (20 [range 10‐41] days; P = .033). The 2019‐nCoV infection is spreading and its incidence is increasing nationwide. The first deaths occurred mostly in elderly people, among whom the disease might progress faster. The public should still be cautious in dealing with the virus and pay more attention to protecting the elderly people from the virus.
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            Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020

            The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409–7496 additional confirmed cases in Hubei and 1128–1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588–13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th – 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.
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              Estimation of COVID-19 prevalence in Italy, Spain, and France

              At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The daily prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the WHO website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.
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                Author and article information

                Journal
                Biomed Signal Process Control
                Biomed Signal Process Control
                Biomedical Signal Processing and Control
                Elsevier Ltd.
                1746-8094
                1746-8094
                10 February 2021
                10 February 2021
                : 102494
                Affiliations
                [a ]Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
                [b ]Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
                Author notes
                [* ]Corresponding author.
                Article
                S1746-8094(21)00091-4 102494
                10.1016/j.bspc.2021.102494
                7874981
                33594301
                6132e364-45e1-4235-ab31-a97152a98bcb
                © 2021 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 1 October 2020
                : 19 November 2020
                : 4 February 2021
                Categories
                Article

                covid-19,hybrid model,nnetar,bsts,arima,forecasting,time series
                covid-19, hybrid model, nnetar, bsts, arima, forecasting, time series

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